Publication Date

Spring 2021

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Katerina Potika

Second Advisor

Mike Wu

Third Advisor

Robert Chun

Keywords

Social Network Graph, Natural Language Processing, Su- pervised Learning, Neural Network Model

Abstract

With the popularity of social media platforms such as Facebook, Twitter, and Instagram, people widely share their opinions and comments over the Internet. Exten- sive use of social media has also caused a lot of problems. A representative problem is Cyberbullying, which is a serious social problem, mostly among teenagers. Cyber- bullying occurs when a social media user posts aggressive words or phrases to harass other users, and that leads to negatively affects on their mental and social well-being. Additionally, it may ruin the reputation of that media. We are considering the problem of detecting posts that are aggressive. Moreover, we try to detect Cyberbullies.

In this research, we study Cyberbullying as a classification problem by combining text mining techniques, and the graph of the social network relationships based on a dataset from Twitter. We create an new dataset that has more information for each tweet (post). We improve the classification accuracy by considering the additional social network features based on the user’s follower list and retweet information.

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